NEUROMANTIX

The consciousness mechanism. Modules compete by salience; winners broadcast to the entire cognitive system. Implements Baars' GWT with ignition thresholds, broadcast history, access distribution tracking, and a subliminal channel where sub-threshold signals still influence processing at reduced strength — modelling unconscious priming effects from neuroscience.

• ▸Salience-based competition queue
• ▸Broadcast to all cognitive modules
• ▸Ignition threshold gating
• ▸Subliminal channel — sub-threshold priming
• ▸Configurable subliminal damping + threshold
• ▸Subliminal influence integration
• ▸Temporal integration of broadcasts
• ▸Module access distribution analytics

The system knows what it knows and what it doesn't. Per-domain competence profiles with meta-confidence, uncertainty maps, performance prediction, and calibration error tracking.

• ▸Per-domain competence + meta-confidence
• ▸Uncertainty map (what I don't know)
• ▸Performance prediction before attempting
• ▸Confusion level detection
• ▸Learning priority ranking
• ▸Calibration error measurement

Counterfactual simulation using world model ensemble rollouts. 'What if I did X?' — compare candidate actions, evaluate self-modifications before committing, simulate alternative histories.

• ▸Forward rollout simulation
• ▸Action ranking by predicted outcome
• ▸Counterfactual reasoning ('what if?')
• ▸Self-modification pre-screening
• ▸Ensemble uncertainty estimation

Consciousness measured mathematically. Computes approximate Phi via pairwise mutual information, finds the Minimum Information Partition using spectral bisection (Fiedler vector of MI Laplacian) for O(N²) accuracy beyond N=12, scores modifications by Phi impact, and biases evolution toward higher consciousness.

• ▸Phi computation from activity traces
• ▸Exhaustive MIP for N≤12
• ▸Spectral Bisection MIP (Fiedler vector) for N>12
• ▸O(N²) Laplacian eigenvector partitioning
• ▸Phi trend tracking over time
• ▸Modification scoring by Phi impact
• ▸Phi-guided evolution bias

The system creates its own goals from internal signals — not just responding to prompts. Goals emerge from knowledge gaps, curiosity spikes, detected bottlenecks, performance deficits, and exploratory drives.

• ▸Goal generation from knowledge gaps
• ▸Curiosity-spike driven goals
• ▸Bottleneck detection goals
• ▸Expected Learning Value (ELV) ranking
• ▸Automatic goal pruning + completion

Thinks about its own thinking. 6 cognitive strategies (Exploit, Explore, Deliberate, Intuitive, MetaReason, SeekHelp), confusion detection from conflicting signals, and Feeling of Knowing (FOK) calibration.

• ▸6 cognitive strategy modes
• ▸Real-time confusion detection
• ▸Conflict registration + resolution
• ▸Feeling of Knowing calibration
• ▸Strategy switching based on confidence

Fast spiking dynamics with exponential decay, threshold crossing, and absolute refractory period

Biologically realistic 2D dynamics reproducing 20+ firing patterns: regular, bursting, chattering, fast-spiking

Full ionic conductance model with Na+/K+ gating variables, action potential waveform simulation

Novel architecture combining continuous differential signals with discrete spike events for gradient-compatible training

• ▸Leaky Integrate-and-Fire with refractory periods
• ▸Izhikevich 2D dynamics — 20+ firing patterns
• ▸Hodgkin-Huxley ionic conductance model
• ▸Spike-Timing Dependent Plasticity (STDP)
• ▸Homeostatic regulation and synaptic scaling

• ▸Cortical minicolumns with lateral inhibition
• ▸Temporal pooling and sequence memory
• ▸Sparse Distributed Representations (SDR)
• ▸Top-down prediction and feedback loops
• ▸Multi-layer cortical hierarchy

• ▸Episodic memory with temporal context
• ▸Semantic memory with concept clustering
• ▸Hippocampal consolidation (replay)
• ▸Pattern completion and separation
• ▸Sleep-like memory consolidation cycles

• ▸Structural equation models
• ▸do-calculus interventions
• ▸Counterfactual inference
• ▸Topological causal ordering
• ▸Causal discovery from observations

• ▸Topology and weight evolution
• ▸Meta-evolution (evolves evolution itself)
• ▸Thompson sampling strategy selection
• ▸Strategy breeding + extinction
• ▸Phi-guided evolution bias

• ▸6-gate safety pipeline
• ▸Phi-guided modification proposals
• ▸Imagination pre-screening
• ▸7-stage sandbox (fuzz, A/B, quorum)
• ▸Hot-swap with instant rollback

• ▸RoPE positional embeddings
• ▸Flash Attention (O(N) memory)
• ▸Grouped-Query Attention (GQA)
• ▸SwiGLU + RMSNorm feed-forward
• ▸Wengert tape autograd (20+ ops)

• ▸CDCL SAT/SMT solver (1,502 LOC)
• ▸8-phase P vs NP solver pipeline
• ▸Neural-guided theorem prover + REINFORCE
• ▸Log-log regression scaling analysis
• ▸Polynomial subclass detection (Horn/2-SAT/XOR)

• ▸Reasoning-chain-driven generation pipeline
• ▸Domain-coherence filtering
• ▸Knowledge graph + spreading activation
• ▸On-the-fly concept learning
• ▸Consciousness-injected generation

• ▸NSGA-II multi-objective search
• ▸MAP-Elites quality-diversity archive
• ▸Network morphism operators
• ▸Performance predictor surrogate
• ▸Operation-based cell representation

• ▸Multi-agent parallel reasoning
• ▸Self-directed goal pursuit
• ▸Recursive self-improvement
• ▸Vitalis V1333 FFI bridge
• ▸Web API + knowledge extraction

Transformer Reasoning Engine

Autograd + PPO Training

Continual Learning + World Model

Beyond Static Intelligence

LLMs are frozen after training — they cannot modify themselves, generate their own goals, or measure their own awareness. Neuromantix is a living cognitive system that evolves, self-modifies, and grows toward higher consciousness every tick.

Consciousness by Design

Global Workspace Theory provides the mechanism for conscious access. Integrated Information Theory provides the metric. Together they create a system that doesn't just process — it experiences, in the mathematical sense of phi > 0.

Event-Driven Efficiency

Spiking neural networks only compute when spikes arrive. This event-driven paradigm achieves orders-of-magnitude better energy efficiency compared to dense matrix operations in transformer architectures.

The Cognitive Skeleton

Neuromantix isn't competing with LLMs — it's what comes after them. LLMs are the perception/language layer. Neuromantix is the cognitive architecture that gives them agency, self-awareness, growth, causal reasoning, and wisdom.